Data Visualization Design, Part 2: Data to Ink Ratio Examples

Last week I discussed one of the data visualization design principles I’ve learned this fall semester at Indiana University’s School of Informatics and Computing. Today I want to give you a few examples of applying Tufte’s Data to Ink ratio. Several ways to maximize the information displayed with the smallest ‘amount of ink’ is to 1) remove backgrounds, 2) remove redundant labels, 3) remove unnecessary borders, 4) reduce colors, 5) remove special effects, 6) remove bolding or using font to communicate information, 7) Add direct labels on the chart and above all remember that 8) ‘Less is more effective.’ This video by Gfycat shows a chart with a lot of ink and then shows how to use each of these eight rules to maximize the data to ink ratio.

Let’s start the discussion by looking at an example of Gun Deaths in Florida designed by Derrick Eckardt.

Before

After

In addition to flipping the x and y axes, the beginning value of the y-axis was changed to 400, while keeping the same distance between tick marks. The red background was removed and the line was changed to red (though arguably this is not the best color choice due to red/green color-blindness). Wording of the title was edited to better reflect the chart data. The subtitle was removed from the original chart since it was fairly similar to the title. The ‘2005’ label was removed since that information can be deduced from the scale. Finally, the x and y-axis label color was changed to a lighter gray following Tufte’s principles of data visualization design.

Here is another example of a “Before” and “After” data visualization of Condom Prices around the World.

The re-designed visualization follows Tufte’s rules of maximizing the data to ink ratio because the following rules were applied: 1. Removed the background of world image as the word “world” in title already conveys the required message, 2. Removed color from bars, 3. The condom image was removed from all bars since this is redundant information (information is already conveyed in the chart title), 4. Removed horizontal and vertical axis lines since ticks are sufficient, and 5. Added circle, points for each data point since circles gives us the same perspective of data before simplification.

Next week I’ll be staying on this data visualization theme by talking about the role of chart junk in data science.